Systems and methods for matching face shapes

ABSTRACT

Systems for matching face shapes may include a computer-readable non-transitory storage medium and an executing hardware unit. The storage medium may include a set of instructions for target object shape matching. The executing hardware unit may be in communication with the storage medium and may be configured to execute the set of instructions. The executing hardware unit may be configured to obtain a target object image for shape matching; determine a shape character of the target object image based on a shape of the target object image; determine similarities between the target object image and a plurality of template images of reference objects based on the shape character of the target object image and shape characters of the reference objects in the plurality of template images; and select a template image from the plurality of template images that has a largest similarity to the target object image.

RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/CN2013/087001, filed on Nov. 13, 2013, in the State IntellectualProperty Office of the People's Republic of China, which claims thebenefit of Chinese Patent Application No. 2013101691888 filed on May 9,2013, the disclosures of which are incorporated herein in their entiretyby reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present disclosure relates to face recognition technology.Specifically, the present disclosure relates to systems and methods formatching face shapes.

2. Background

With developments of computer technology, face recognition technologybecomes more and more mature. The present face recognition refers tocomputer technology which makes use of analysis and comparison of visualfeature information of human face for authentication. Shape is one ofthe most direct expressions of an object. Objects with similar shapesare everywhere in daily life, and human faces are often used incomparison with other objects. For example, sometimes people describethe face of a person as an egg-shaped face or an oval face; andsometimes we just feel that the face of someone resembles a face of apet dog. Describing human faces with particular objects is fun. It alsomakes the facial expression more profoundly understood.

SUMMARY OF THE INVENTION

According to an aspect of the present disclosure, a system for matchingface shapes may comprise a computer-readable non-transitory storagemedium and an executing hardware unit. The storage medium may comprise aset of instructions for target object shape matching. The executinghardware unit may be in communication with the storage medium and may beconfigured to execute the set of instructions. The executing hardwareunit may be configured to obtain a target object image for shapematching; determine a shape character of the target object image basedon a shape of the target object image; determine similarities betweenthe target object image and a plurality of template images of referenceobjects based on the shape character of the target object image andshape characters of the reference objects in the plurality of templateimages; and select a template image from the plurality of templateimages that has a largest similarity to the target object image.

According to an aspect of the present disclosure, a computer-implementedmethod for shape matching may comprise obtaining, by at least onecomputer hardware unit, a target object image for shape matching;determining, by at least one computer hardware unit, a shape characterof the target object image based on a shape of the target object image;determining, by at least one computer hardware unit, similaritiesbetween the target object image and a plurality of template images ofreference objects based on the shape character of the target objectimage and shape characters of the reference objects in the plurality oftemplate images; and selecting, by at least one computer hardware unit,a template image from the plurality of template images that has alargest similarity to the shape of the target object image.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other features and advantages will become more apparent bydescribing in detail example embodiments thereof with reference to theattached drawings in which:

FIG. 1 illustrates an ASM iterative processing model for image shapeacquisition;

FIG. 2 illustrates a Snake model for extracting a shape;

FIG. 3 illustrates a Canny model for obtaining image edges;

FIG. 4 is a flow diagram of a method for matching face shapes accordingto example embodiments of the present disclosure;

FIG. 5 is a flow diagram of another method for matching face shapesaccording to the example embodiments of the present disclosure;

FIG. 6 is a schematic diagram of geometric blurring according to theexample embodiments of the present disclosure;

FIG. 7 is a schematic diagram of extracting human face shape accordingto the example embodiments of the present disclosure;

FIG. 8 is a schematic diagram of a system for matching human face shapesaccording to the example embodiments of the present disclosure;

FIG. 9 is a schematic diagram of a system for matching human face shapesaccording to the example embodiments of the present disclosure;

FIG. 10 is a schematic diagram of a Template Image Database Establishingmodule according to example embodiments of the present disclosure; and

FIG. 11 is a schematic diagram illustrating an example embodiment of acomputer.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Example embodiments will now be described more fully with reference tothe accompanying drawings, in which the example embodiments are shown.The example embodiments may, however, be embodied in many differentforms and should not be construed as being limited to the exampleembodiments set forth herein; rather, the example embodiments areprovided so that this disclosure will be thorough and complete, and willfully convey the concept of the invention to one skilled in the art. Thedrawings may be exaggerated for clarity and not necessarily in scale.Like reference numerals in the drawings denote like elements, and thus,their description will not be repeated.

FIG. 11 is a schematic diagram illustrating an example embodiment of acomputer that may execute methods of the present disclosure. A computer1100 may be a computing device capable of executing a software system.The computer 1100 may, for example, be a device such as a personaldesktop computer or a portable device, such as a laptop computer, atablet computer, a cellular telephone, or a smart phone. The computer1100 may also be a server that connects to the above devices locally orvia a network.

The computer 1100 may vary in terms of capabilities or features. Claimedsubject matter is intended to cover a wide range of potentialvariations. For example, the computer 1100 may comprise a visualinterface 1152, such as a camera. It may also comprise a keypad/keyboard1156 and a display 1154, such as a liquid crystal display (LCD), or adisplay with a high degree of functionality, such as a touch-sensitivecolor 2D or 3D display. In contrast, however, as another example, aweb-enabled computer 1100 may include one or more physical or virtualkeyboards, and mass storage medium 1130.

The computer 1100 may also comprise or may execute a variety ofoperating systems 1141, including an operating system, such as aWindows™ or Linux™, or a mobile operating system, such as iOS™,Android™, or Windows Mobile™. The computer 1100 may comprise or mayexecute a variety of possible applications 1142, such as an electronicgame 1145. An application 1142 may enable communication with otherdevices via a network, such as communicating with another computer viaan Internet network for online electronic games.

Further, the computer 1100 may comprise one or more non-transitoryprocessor-readable storage media 1130 and one or more processors 1122 incommunication with the non-transitory processor-readable storage media1130. For example, the non-transitory processor-readable storage media1130 may be a RAM memory, flash memory, ROM memory, EPROM memory, EEPROMmemory, registers, hard disk, a removable disk, a CD-ROM, or any otherform of non-transitory storage medium known in the art. The one or morenon-transitory processor-readable storage media 1130 may store sets ofinstructions, or units and/or modules that comprise the sets ofinstructions, for conducting operations described in the presentapplication. The one or more processors may be configured to execute thesets of instructions and perform the operations in example embodimentsof the present application.

For example, the computer 1100 may comprise a main system module device,which may be comprised of the non-transitory storage medium, and aprocessor, which may be configured to execute a main system process. Thecomputer 1100 may also comprise a subsystem process module device, whichmay also be comprised of a non-transitory memory and a processorconfigured to execute a subsystem process and to generate a userinterface. The main system module device may create the subsystemprocess such that it is independent from the main system process. Themain system module device may receive and send a user input to thesubsystem process module device. The main system module device maygenerate a first computation result according to the user input. Thesubsystem module device may generate a second computation result that isin parallel with the main system process according to the user input.The main system process module device may receive the second computationresult. The main system module device then may combine the firstcomputation result and the second computation result as explained inmore detail herein.

Merely for illustration, only one processor will be described incomputers that execute operations in the following example embodiments.However, it should be note that the computers in the present applicationmay also comprise multiple processors, thus operations that areperformed by one processor as described in the present application mayalso be jointly or separately performed by the multiple processors. Forexample, if in the present application a processor of a computerexecutes both step A and step B, it should be understood that step A andstep B may also be performed by two different processors jointly orseparately in the computer (e.g., the first processor executes step Aand the second processor executes step B, or the first and secondprocessors jointly execute steps A and B).

FIG. 1 illustrates an ASM iterative processing for image shapeacquisition. The ASM (Active Shape Model) Algorithm is an algorithm forpositioning the shape of an object. The method may be stored in thenon-transitory processor readable storage media 1130 as a set ofinstructions and executed by the processor 1122 in FIG. 11. Theexecution process may direct the processor 1122 to perform the followingacts: first based on a reference shape of a human face, training and/orapplying a global shape model and a local texture model, respectively,wherein the reference shape may be an image of a human face of which itsshape has been demarcated; and then obtaining the position of the shapeof the human face image through the following steps:

1) Setting an average shape of the global shape model as an initial faceshape of a human being, with the same eye positions between the initialshape and the image, wherein the average shape may comprise by aplurality of shape point describing the face shape of a human being. Forexample, the average shape may be obtained by prior trainings. The priortrainings may also obtain shape model and texture model. For example,the face shape may comprise border lines (e.g., edges) of the face,nose, eyes of the face of the human being. After detecting the face onthe image and locating eye position of the face on the image, theprocessor 1122 may set the eye positions of the initial shape to the eyepositions of the face on the image, thereby applying the initial shapeto the image.

2) Starting from the initial shape and based on the local texture model,searching in the vicinity of each shape point for the candidate point ofwhich the texture feature is most similar to the texture feature of theshape point. For example, there may be a texture model that correspondsto each point of the initial shape. The texture model may provide ascore for each point position. In all the neighbor points, the processor1122 may choose one most similar to the texture feature of the shapepoint (e.g., with the highest score) as the candidate point.

3) Obtaining new face shape points by executing shape constraints andcorrection on all the searched shape points based on the global shapemodel.

4) Repeating steps 2)-3) until the difference of the shape pointsobtained by the most recent two iterations is less than a giventhreshold.

FIG. 2 illustrates a Snake model for extracting a shape. The Snake modelalgorithm is an iterative algorithm. Starting from a given group ofinitial points 280(a), the Snake model may be used on a shape to achievemaximum gradient of the whole shape. The algorithm may be stored in thenon-transitory processor readable storage media 1130 as a set ofinstructions and executed by the processor 1122 in FIG. 11. The storedalgorithm may direct the processor 1122 to perform the following acts:

1) Arbitrarily initializing a group of shape points 280(a), wherein thegroup of shape points 280 forms an initial shape and each shape point inthe group of shape points corresponds with an image pixel;

2) For each shape point in the group of shape points 280(a), computing agradient direction of the corresponding image pixel, and move the shapepoint along the direction of the gradient. The moved group of shapepoints may result an updated shape. For example, the group of shapepoints 280(a) may be an updated shape of the group of shape points280(b);

3) Computing an overall energy function of the updated shape and adjustthe updated shape accordingly. The energy function may be expressed as,E=f(x,y)=Σ(α(x,y)′+β(x,y)″−|∇I(x,y)|²)where (x,y)′ and (x,y)″ are first-order and second-order derivatives ofpoint (x,y), and ∇I(x,y) are divergence of this point.

4) Repeating steps 2)-3) until the updated shape converges to the shape290 of the image. FIG. 2( a) to FIG. 2( e) illustrates the course of theconvergence.

FIG. 3 illustrates a Canny model for obtaining image edges. Canny is amethod of gaining image edges. An algorithm implementing the Canny modelmay be stored in the non-transitory processor readable storage media1130 as a set of instructions and executed by the processor 1122 in FIG.11. The stored algorithm may direct the processor 1122 to perform thefollowing acts:

1) Executing Gaussian blurring on an image to remove image noises;

2) Applying four masks on the image to respectively execute imageconvolution along horizontal, vertical and two diagonal directions ofthe image; and then identifying those brighter points in the image asthe candidate edge points;

3) Obtaining the image shape by selecting the edge points.

FIG. 4 is a flow diagram of a method for matching facial shapesaccording to example embodiments of the present disclosure. The methodmay be stored in the non-transitory processor readable storage media1130 as a set of instructions and executed by the processor 1122 in FIG.11. The stored method may direct the processor 1122 to perform thefollowing acts:

101. Obtaining a sample of visual document to be tested. For example,the sample may be an image, such as a face image of human being. Thesample may also be a picture or video stream.

102. Executing face detection on the sample to obtain and/or determinecharacters of the human face. For example, the characters of the humanface may be characters of the face shape (shape characters). The faceshape may be border lines (e.g., edges) of the face, nose, eyes of theface of the human being, or a combination thereof. For example, if aperson has a big nose than average persons, the border lines of his/hernose in an image may be bigger than an image of an ordinary person andmay be a character of his/her face shape. Further, the face shape maycomprise by a plurality of shape points describing the border lines.Accordingly, the shape characters of a face may be represented by theplurality of discrete shape points and/or coordinates of the pluralityof shape points of the face shape. For example, if any two shape pointsthat are adjacent to each other and are along a border line of the sameorgan form a vector, the shape characters of the face may be describedby a group of vectors;

103. Matching and/or comparing the shape characters of the human facewith shape characters of all template images stored in a template imagedatabase one by one, and obtaining a template image that may resemblemost similar to the human face. The template image database may bestored in one or more non-transitory processor-readable storage media1130.

Prior to obtaining the sample image to be tested, the method may alsodirect the processor 1122 to perform acts of:

Establishing the template image database. The template image databasemay comprise shape characters of template images.

Establishing the template image database may comprise:

Obtaining images of various objects with various shapes, capturing themain body of each image, and obtaining standardized template images forthese objects. The main body of an image may be a portion of the objectin the image that is of interest. For example, if the image is a halfportrait of a dog, the main body of the image may be the face and/orhead of the dog. A standardized template image may be an image thatincludes only the main body. The standardized template image may also betuned and/or cropped from the original image so that the main body inthe standardized image satisfied certain pre-determined requirements.For example, a standardized template image may be defined as an imagewith a size of 1000×1000 pixels, wherein the main body is in the centerthereof and the upper and lower boundaries of the main body are 100pixels away from the upper and lower boundaries of the standardizedtemplate image, respectively;

Extracting and/or determining the shape characters for everystandardized template image;

Saving the shape characters corresponding to every standardized templateimage in the template image database.

According to the example embodiments of the present disclosure, thestandardized template images comprise but not limited to pet classimages and/or food class images.

According to the example embodiments of the present disclosure, theprocessor may extract and/or determine the shape characters of everystandardized template image, the method may comprise:

Obtaining the main shape of every standardized template image. Forexample, when there are multiple objects in the standardized templateimage, the shape in the middle of the image may be choose as the mainshape;

Extracting and/or determining shape characters of the template imageaccording to the main shape of the template image. The shape charactersof the template image may have the same definition as the shapecharacters of the face shape.

In order to obtain the main shape of every standardized template image,the method may direct the processor 1122 to perform acts of:

Obtaining the main shape of the standardized template image by trainingthe processor 1122 with ASM Algorithm of face shapes when thestandardized template image is pet class image (e.g., applying the ASMAlgorithm on the standardized template image);

Obtaining the main shape of the standardized template image by trainingthe processor 1122 with the Snake Model Algorithm for positioning theouter boundary shape when the standardized template image is food classimage (e.g., applying the Snake Model Algorithm on the standardizedtemplate image);

When the standardized template image is an image with a fuzzy contour oredge, obtaining the edge of the main shape by Canny edge detectionmethod (e.g., applying the Canny edge detection method on thestandardized template image). The edge of the main shape obtained byCanny edge detection method may not be smooth enough, i.e., the edge maycomprise a plurality of small edges (i.e., noises, e.g., small edges 391in FIG. 3), where in the small edges may be edges comprising edge lengthless than a given threshold value. Accordingly, the processor 1122 maysmooth the main shape edge by filtering out the small edges.

In order to extract and/or determine shape characters of the templateimage according to the main shape of the template image, the method maydirect the processor 1122 to perform acts of:

Conducting geometric blurring on the main shape of every template imageto obtain a blurred shape that corresponds to the template image;

Conducting sampling on the blurred shapes that corresponds to thetemplate image, and obtaining the shape characters of the templateimage. The shape characters may comprise coordinates of discrete pointsand descriptors of the geometric blurring (“geometric blurringdescriptors”).

In order to match and/or compare the shape characters of the human facewith the shape characters of all the template images in the templateimage database one by one to obtain the template image that is mostsimilar to the human face, the method may direct the processor 1122 toperform acts of:

Obtaining differences between blurring operators for the human faceshape characters and blurring operators for shape characters of everytemplate image, based on the geometric blurring descriptors of the humanface shape characters and the geometric blurring descriptors of everytemplate image in the template image database. The blurring operatorsare described in step 202.

Obtaining differences of the matching shape between the shape charactersof human face and the shape characters of every template image in thetemplate image database according to the discrete point coordinates ofthe shape characters of the human face and the discrete pointcoordinates of every template image (i.e., obtaining difference of theshape between human face and the reference object in the templateimage).

Obtaining similarities between the shape characters of human face andthe shape characters of every template image according to the abovedifferences of blurring operators and the above differences of matchingshape.

Obtaining the maximum similarity value from the above obtainedsimilarities and providing the template image corresponding to thismaximum similarity value as the most similar template image to the humanface.

Thus, according to the example embodiments of the present disclosure,the processor 1122 may execute face detection on samples of the imagesto obtain the shape characters of the human face; match and/or comparethe shape characters of the human face with the shape characters of allthe template images in the template image database one by one to obtainthe template image that is most similar to the human face, thusrealizing the detection of objects similar to the human faces.

FIG. 5 is a flow diagram of a method for matching facial shapesaccording to example embodiments of the present disclosure. The methodmay be stored in the non-transitory processor readable storage media1130 as a set of instructions and executed by the processor 1122 in FIG.11. The stored method may direct the processor 1122 to perform thefollowing acts:

201. Obtaining images of various objects with different shapes andcapturing the main body of each object image to obtain standardizedtemplate image.

According to the example embodiments of the present disclosure, theprocessor 1122 may establish a template image database comprisingobjects with different shapes, and may retrieve in the database theobject of which the shape resembles most similar to the shape of theface for any human being. The template images may be categorizedaccording to their shapes. For example, the template images may becategorized as, but are not limited to, animal, food, furniture, andplant, etc. For illustration purposes only, the example embodiments inthe present disclosure only involve two categories: pet class and foodclass. Pet class images may comprise different types of pets such ascats, dogs, mice, etc.; food class images may comprise pictures ofobjects in daily life, such as watermelon seeds, duck eggs, etc.

The processor 1122 may obtain images of objects with different shapes,such as cats, dogs, watermelon seeds, etc. The processor 1122 then maycapture image of the main body in these object images, such as pet'sfaces, to obtain a standardized template image. After obtaining thestandardized template image, the processor 1122 may execute lightingoperations and theme strengthening operations on the template images tomake each of the template images only contain a single object under asingle background, and reduce the background information.

202. Extracting the shape characters of every standardized templateimage.

In this step, the processor 1122 may extract and/or determine the shapecharacters of every standardized template image. To this end, theprocessor 1122 may first obtain the main shape of every standardizedtemplate image; and then extract and/or determine the shape charactersof the template image according to the main shape of the template image.

According to the example embodiments of the present disclosure, thestandardized template images may be categorized into three classes: thefirst class of images may be human face images including clear petimages, which may have the similar facial characters to that of thehuman face, the main shape in the first class images may be obtained viatraining the processor 1122 under ASM algorithm. The second class ofimages may be images with clear main contours, including food classimages that have a simple shape. The main shape of the second classimages may be obtained via training the processor 1122 under the Snakemodel algorithm to identify the boundary of the main shape. The thirdclass images may be images with a fuzzy contour. The main shape of thesecond class images may be obtained by first training the processor 1122under Canny edges detection method to obtain the contour of the mainshape, and then further filtering out small edges of the main shape.

In obtaining the main shape of every standardized template image, theprocessor 1122 may perform the acts of:

Obtaining the main shape of the standardized template image by trainingthe processor 1122 with the ASM Algorithm of face shapes when thestandardized template image is pet class image;

Obtaining the main shape of the standardized template image by trainingthe processor 1122 with Snake Model Algorithm to position the outerboundary shape when the standardized template image is food class image;

Obtaining the main shape of the standardized template image byperforming Canny edge detection method to obtain the edges of thetemplate image and then filtering out the small edges of the edges ofthe template image, when the standardized template image is the imagewith a fuzzy contour.

Furthermore, after obtaining the main shape of the template image, theprocessor 1122 may extract and/or determine the shape characters of thetemplate image according to the main shape of the template image. Tothis end, the processor 1122 may perform the acts of:

Conducting geometric blurring on the main shape of every template imageto obtain blurred shape corresponding to the template image;

Conduct sampling on the blurred shapes corresponding to the templateimage to obtain the shape characters of the template image which includediscrete point coordinates and geometric blurring descriptors, which areintroduced below.

According to the example embodiments of the present disclosure, afterobtaining the face shape points, the processor 1122 may executegeometric blurring on the face shape and extend the original shape intoa shape with redundancy change so as to obtain the shape characters ofthe human face.

FIG. 6 shows the schematic diagram of blurring an original shape 602 toa blurred shape 604 by conducting geometric blurring on the shape. Theprocessor 1122 may adjust a blur factor B_(x) ₀ (x) based on thedistance between a blur point x₀ and the corresponding original shapepoint in the blurring process. The farther the blur point from theoriginal shape point, the larger the blur factor is, i.e., the moresevere blurring.B _(x) ₀ (x)=S _(ax+b)(x ₀ −x),  1)wherein x₀ represents the original shape point, x represents the blurpoint, B_(x) ₀ (x) represents the blurriness of x compared to x₀. Forarbitrary point x₀, blur point x may exist in any direction ax+b of thepoint. Here ax+b represents a straight line that passes through point x.Thus straight lines ax+b with different values of a and b may pointtowards different directions, as shown by 610 a-610 in FIG. 6. Next, theprocessor 1122 may conduct sampling on each of the straight lines to geta group of blur points x′ along the straight lines, wherein theblurriness of points x₀ compared to x is B_(x) ₀ (x). A number of groupsof discrete blur points and blurriness of every point compared to theoriginal point may be obtained based on different a and b. After that,the processor 1122 may conduct sampling on the blurred shape to acquirethe image features represented by discrete point coordinates andgeometric bluffing descriptors.

203. Saving the shape characters corresponding to every standardizedtemplate image in the template image database.

According to the example embodiments of the present disclosure, afterextracting and/or determining the shape characters of the templateimage, the processor 1122 may save every template image and itscorresponding shape characters in the template image database, so thatthe processor 1122 may search and retrieve the face shape characters.

As set forth above, steps 201-203 are the processes of establishing thetemplate image database, which comprises the shape characters oftemplate images. Steps 201-203 may not need to be repeated if thetemplate image database has been established.

204. Obtaining samples of the image to be tested, wherein the image mayinclude a human face, and executing face detection on samples of theimage to obtain the shape characters of the human face.

In this step, the processor 1122 may execute face shapes matching andobtain samples of the images to be tested, wherein the images to betested may be pictures or video streams collected by cameras.

After obtaining a sample image to be tested, the processor 1122 mayfirst conduct face detection and/or locating eyes on the image. Forexample the processor 1122 may obtain the position of a frame of theface in the image by adopting method of adaboost classifier and haarfeatures, and then further locate the positions of the eyes in the frameof the face, and then determine the face shape via ASM algorithm. Theface shape may comprise, but is not limited to, a plurality of points(shape points) locating on boundary of organs on the face, such ascontour of the face, eyes, nose, and mouth. As shown in FIG. 7, afterconducting positioning on the face shape via ASM algorithm, theprocessor 1122 obtains the face shape points 710.

Furthermore, the processor 1122 may extract and/or determine the shapecharacters of the human face according to the face shape. To this end,the processor 1122 may execute geometric blurring on the face shape toobtain a blurred shape 720 corresponding to the human face; and conductsampling on the blurred shapes corresponding to the human face to obtainthe shape characters of the human face, the face shape may includediscrete point coordinates and geometric blurring descriptors, etc.

The blurring process may be similar to the blurring process of thetemplate image in step 201.

205. Matching and/or comparing the shape characters of the human facewith the shape characters of all the template images in the templateimage database one by one to obtain the template image that is mostsimilar to the human face.

After obtaining the shape characters of the human face in this step, theprocessor 1122 may match and/or compare the shape characters with theshape characters of every image in the image template in order toacquire an object which is most similar to the human face.

To this end, the processor 1122 may match and/or compare the shapecharacters of the human face with the shape characters of all thetemplate images in the template image database one by one to obtain thetemplate image that is most similar to the human face. Accordingly, theprocessor 1122 may perform acts of:

Obtaining the difference between bluffing operators of the shapecharacters of human face and bluffing operators of the shape charactersof every template image in the template image database, according to thegeometric bluffing descriptors of the shape characters of the human faceand the geometric bluffing descriptors of every template image.

Obtaining a shape difference between the shape characters of human faceand shape characters of every template image, according to the discretepoint coordinates of the shape characters of the human face and thediscrete point coordinates of every template image.

Obtaining the similarity between the shape characters of human face andthe shape characters of every template image in the template imagedatabase according to the difference of blurring operators between theshape characters of the human face and the shape characters of everytemplate image, and according to the difference of matching shapebetween the shape characters of human face and the shape characters ofevery template image.

Obtaining the maximum similarity value from the obtained similaritiesand output the template image corresponding to this value as the mostsimilar template image to the human face.

According to the example embodiments of the present disclosure, thesimilarity Dist(x₁,x₂) between any two groups of features may becompleted by the bluffing operator difference and the difference ofmatching shape, as shown in Equation 2):Dist(x ₁ ,x ₂)=w _(m) f _(m)(x ₁ ,x ₂)+w _(d) f _(d)(x ₁ ,x ₂);  2)wherein w_(m) and f_(m) correspond to the differential weight ofgeometric bluffing and difference function in the features of the twogroups, respectively; w_(d) and f_(d) correspond to the differentialweight of shape and difference value between the features, respectively;the sum of w_(m) and w_(d) is 1.

Because each group of the shape characters may be represented bypositions of discrete points in the shape, any two closely adjacentpoints (e.g., points along the border of nose, face, and eyes) may forma vector. Therefore each group of shape characters may be expressed by agroup of such vectors. For any two groups of the shape characters, theprocessor 1122 may determine the difference between corresponding pairsof vectors in the two groups (e.g., the processor 1122 may compare avector in the first group with a vector in the second group thatcorresponds to the vector in the first group). The difference may beexpressed as a vector as well (e.g., a vector pointing from one vectorto the other vector). The processor 1122 may determine two eigenvaluesfor each difference: intersection angle of vectors d_(a) and lengthdifference d_(l), so the difference of matching shape characters are:f _(d)(x ₁ ,x ₂)=Σrd _(a)(x ₁ ,x ₂)+Σ(1−r)d _(l)(x ₁ ,x ₂);  3)

The difference of geometric bluffing operators are determined byblurriness of every pixel,f _(m)(x ₁ ,x ₂)=f(B _(x) ₁ ,B _(x) ₂ );  4)

According to the example embodiments of the present disclosure, theprocessor 1122 may compute the similarity Dist(x₁,x₂) between two groupsof shapes according to the difference of blurring operators of shapecharacters of the human face in the image to test and the difference ofblurring operators of shape characters of each template image. Then theprocessor 1122 may find the maximum similarity value based on thesimilarities Dist(x₁,x₂) between the image to test and the templateimages in the template image database, and retrieve the template imagewhich is most similar to the entered human face in the image to test,treat the template as the output template image.

FIG. 8 is a schematic diagram of a system for matching human face shapesaccording to example embodiments of the present disclosure. The systemmay comprise: Testing Sample Acquisition module 301, Face ShapeExtraction module 302, and Face Shape Matching module 303. TestingSample Acquisition module 301 may be configured to obtain a sample of animage of human face to be tested; Face Shape Extraction module 302 maybe configured to detect on the sample to obtain the shape character ofthe human face; and Face Shape Matching module 303 may be configured tomatch and/or compare the shape characters of the human face with theshape characters of all the template images in the template imagedatabase one by one to obtain the template image that is most similar tothe human face.

FIG. 9 is a schematic diagram of a system for matching human face shapesaccording to example embodiments of the present disclosure. In additionto the modules in FIG. 8, the system in FIG. 9 may further compriseTemplate Image Database Establishing module 304, which may be configuredto establish the template image database before the sample of the imageto be tested were obtained by Testing Sample Acquisition module 301.

FIG. 10 is a schematic diagram of the Template Image DatabaseEstablishing module 304 according to example embodiments of the presentdisclosure. The Template Image Database Establishing module may compriseStandardized Template unit 304 a, which may be configured to obtainimages of objects in different shapes and capturing the main body of theobject in each image to obtain a standardized template image; ShapeExtraction unit 304 b, which may be configured to extract and/ordetermine the shape characters of every standardized template image; andSaving unit 304 c, which may be configured to save the shape characterscorresponding to every standardized template image in the template imagedatabase. According to the example embodiments of the presentdisclosure, the standardized template images may comprise, but are notlimited to, pet class images and/or food class images.

Further, as shown in FIG. 10, Shape Extraction unit 304 b may compriseMain Shape Extraction subunit 304 b 1, which may be configured to obtainthe main shape of every standardized template image; and Shape FeatureExtraction subunit 304 b 2, which may be configured to extract and/ordetermine the shape characters of the template image according to themain shape of the template image.

The Main Shape Extraction subunit 304 b 1 may be configured to:

Obtain the main shape of the standardized template image by training aprocessor with the ASM Algorithm of face shapes when the standardizedtemplate image is pet class image;

Obtain the main shape of the standardized template image by training theprocessor with Snake Model Algorithm for positioning the outer boundaryshape when the standardized template image is food class image;

The main shape of the standardized template image may be obtained byCanny edge detection method for obtaining the edges of the templateimage and then by filtering out the small edges of the edges of thetemplate image when the standardized template image is the image with afuzzy contour.

The Shape Feature Extraction subunit 304 b 2 may be configured to:

Conduct geometric bluffing on the main shape of every template image toobtain blurred shape corresponding to the template image;

Conduct sampling on the blurred shapes corresponding to the templateimage to obtain the shape characters of the template image which includediscrete point coordinates and geometric bluffing descriptors.

Referring back to FIG. 9, the Face Shape Matching module 303 maycomprise:

Bluffing Operator Difference Acquisition unit 303 a, which may beconfigured to obtain the difference between the bluffing operator of theshape characters of the human face and the bluffing operator of theshape characters of every template image, according to the geometricblurring descriptors of the shape characters of the human face and thegeometric bluffing descriptors of every template image in the templateimage database.

Matching Shape Difference Acquisition unit 303 b, which may beconfigured to obtain the shape difference between the shape charactersof human face and the shape characters of every template image in thetemplate image database according to the discrete point coordinates ofthe shape characters of the human face and the discrete pointcoordinates of every template image.

Similarity Acquisition unit 303 c, which may be configured to obtain thesimilarities between the shape characters of human face and the shapecharacters of every template image according to the difference betweenthe bluffing operator of the shape characters of the human face and theblurring operator of the shape characters of every template image, andaccording to the difference of matching shape between the shapecharacter of human face and the shape character of every template image.

Matched Object Retrieval unit 303 d, which may be configured to obtainthe maximum similarity value from the obtained similarities andoutputting the template image corresponding to this value as the mostsimilar template image to the human face.

While the systems for face shape matching adopt the above respectivemodules, in the actual application, the above functions may adoptdifferent modules to complete similar functions as required. Forexample, the system may assign different internal structures intodifferent function modules to complete all or part of the functionsdescribed above.

Additionally, an ordinary skill in the art will understand that therealization of all or part of the above systems and methods may beachieved via hardware or by programs instructing related hardware (e.g.,the processor 1122). The program may be store in one or morecomputer-readable storage media that may be a read-only memory, a diskor a CD-ROM, etc.

While example embodiments of the present disclosure relate to systemsand methods of face shape matching, the systems and methods may also beapplied to other applications. For example, in addition to face shapematching, the systems and methods may also be applied in shapeidentification or shape recognition of other objects. The presentapplication intends to cover the broadest scope of image and/or shapeidentification technologies that a computer process may obtain.

Thus, example embodiments illustrated in FIGS. 1-11 serve only asexamples to illustrate several ways of implementation of the presentdisclosure. They should not be construed as to limit the spirit andscope of the example embodiments of the present disclosure. It should benoted that those skilled in the art may still make various modificationsor variations without departing from the spirit and scope of the exampleembodiments. Such modifications and variations shall fall within theprotection scope of the example embodiments, as defined in attachedclaims.

What is claimed is:
 1. A system for shape matching, comprising: at leastone computer-readable non-transitory storage medium, comprising a set ofinstructions for target object shape matching; and at least oneexecuting hardware unit in communication with the at least onecomputer-readable non-transitory storage medium that is configured toexecute the set of instructions and is configured to: obtain a targetobject image for shape matching; determine a shape character of thetarget object image based on a shape of the target object image;determine similarities between the target object image and a pluralityof template images of reference objects based on the shape character ofthe target object image and shape characters associated with theplurality of template images; and select from the plurality of templateimages a template image that has a largest similarity to the targetobject image; wherein each of the plurality of template image is animage of a reference object and is associated with a shape character ofthe image of the reference object; wherein the shape character of theimage of the reference object comprises coordinates of a plurality ofdiscrete points locating along edges of components on the image of thereference object and a plurality of geometric blurring descriptors of ashape of the reference object; and wherein the shape character of thetarget object image comprises coordinates of the plurality of discretepoints locating along edges of components on the target object image anda plurality of geometric blurring descriptors of the shape of the targetobject.
 2. The system of claim 1, wherein the at least one executinghardware unit is further configured to: obtain a template image of theplurality of template images; obtain a standardized template image fromthe template images; and determine the shape character of the image ofthe reference object based on the standardized template image.
 3. Thesystem of claim 2, wherein to obtain the shape character of the image ofthe reference object, the at least one executing hardware unit isfurther configured to: obtain a main shape of the reference object inthe standardized template image; and determine the shape character ofthe main shape, wherein the main shape is a predetermined portion of thereference object.
 4. The system of claim 2, wherein each of thestandardized template images is categorized as at least one of a petclass image and a food class image.
 5. The system of claim 3, wherein toobtain the main shape of the standardized template image, the at leastone executing hardware unit is further configured to: apply an ActiveShape Model Algorithm on the standardized template image when thereference object is an animal.
 6. The system of claim 3, wherein toobtain the main shape of the standardized template image, the at leastone executing hardware unit is further configured to: apply a SnakeModel Algorithm on the standardized template image to position an outerboundary of the image of the reference object when the reference objectis food.
 7. The system of claim 3, wherein when the standardizedtemplate image is an image with a fuzzy contour to obtain the main shapeof the standardized template image, the at least one executing hardwareunit is further configured to: apply a Canny edge detection method onthe standardized template image to obtain overall edges of the image ofthe reference object; and filter out noises on the overall edges.
 8. Thesystem of claim 3, wherein to determine the shape character of thetemplate image the at least one executing hardware unit is furtherconfigured to: obtain a blurred shape corresponding to the templateimage by conducting geometric blurring on the main shape of the image ofthe reference object in the template image; determine the shapecharacter of the template image by conducting sampling on the blurredshape corresponding to the template image.
 9. The system of claim 1,wherein to determine the similarity between the target object image anda template image of the plurality of template images, the at least oneexecuting hardware unit is further configured to: obtain a firstdifference that reflects a difference between a blurring operator of theshape character of the target object image and a blurring operator ofthe shape character of the template image according to the geometricblurring descriptors of the shape character of the target object imageand the geometric blurring descriptors of the shape character of thetemplate image; obtain a second difference that reflects a differencebetween the shape character of the target object image and the shapecharacter of the template image according to the coordinates of theplurality of discrete points of the shape character of the target objectimage and the coordinates of the plurality of discrete points of thetemplate image; and obtain the similarity between the shape character ofthe target object and the shape character of the template imageaccording to the first difference and the second difference.
 10. Acomputer-implemented method for shape matching, comprising: obtaining,by at least one computer hardware unit, a target object image for shapematching; determining, by at least one computer hardware unit, a shapecharacter of the target object image based on a shape of the targetobject image; determining, by at least one computer hardware unit,similarities between the target object image and a plurality of templateimages of reference objects based on the shape character of the targetobject image and shape characters associated with the plurality oftemplate images; and selecting, by at least one computer hardware unit,from the plurality of template images a template image that has alargest similarity to the shape of the target object image; wherein eachof the plurality of template image is an image of a reference object andis associated with a shape character of the image of the referenceobject; wherein the shape character of the image of the reference objectcomprises coordinates of a plurality of discrete points locating alongedges of components on the image of the reference object and a pluralityof geometric blurring descriptors of a shape of the reference object;and wherein the shape character of the target object image comprisescoordinates of the plurality of discrete points locating along edges ofcomponents on the target object image and a plurality of geometricblurring descriptors of the shape of the target object.
 11. Thecomputer-implemented method of claim 10, further comprising: obtaining,by at least one computer hardware unit, a template image of theplurality of template images; obtaining, by at least one computerhardware unit, a standardized template image from the template images;and determining, by at least one computer hardware unit, the shapecharacter of the image of the reference object based on the standardizedtemplate image.
 12. The computer-implemented method of claim 11, whereinobtaining the shape character of the image of the reference objectcomprising: obtaining, by at least one computer hardware unit, a mainshape of the reference object in the standardized template image; anddetermining, by at least one computer hardware unit, the shape characterof the main shape, wherein the main shape is a predetermined portion ofthe reference object.
 13. The computer-implemented method of claim 11,wherein each of the standardized template images is categorized as atleast one of a pet class image and a food class image.
 14. Thecomputer-implemented method of claim 12, wherein obtaining the mainshape of the standardized template image comprising: applying, by atleast one computer hardware unit, an Active Shape Model Algorithm on thestandardized template image when the reference object is an animal. 15.The computer-implemented method of claim 12, wherein obtaining the mainshape of the standardized template image comprising: applying, by atleast one computer hardware unit, a Snake Model Algorithm on thestandardized template image to position an outer boundary of the imageof the reference object when the reference object is food.
 16. Thecomputer-implemented method of claim 12, wherein when the standardizedtemplate image is an image with a fuzzy contour obtaining the main shapeof the standardized template image comprising: applying, by at least onecomputer hardware unit, a Canny edge detection method on thestandardized template image to obtain overall edges of the image of thereference object; and filtering out noises on the overall edges.
 17. Thecomputer-implemented method of claim 12, wherein determining the shapecharacter of the template image comprising: obtaining, by at least onecomputer hardware unit, a blurred shape corresponding to the templateimage by conducting geometric blurring on the main shape of the image ofthe reference object in the template image; determining, by at least onecomputer hardware unit, the shape character of the template image byconducting sampling on the blurred shape corresponding to the templateimage.
 18. The computer-implemented method of claim 10, whereindetermining the similarity between the target object image and atemplate image of the plurality of template images comprising:obtaining, by at least one computer hardware unit, a first differencethat reflects a difference between a blurring operator of the shapecharacter of the target object image and a blurring operator of theshape character of the template image according to the geometricblurring descriptors of the shape character of the target object imageand the geometric blurring descriptors of the shape character of thetemplate image; obtaining, by at least one computer hardware unit, asecond difference that reflects a difference between the shape characterof the target object image and the shape character of the template imageaccording to the coordinates of the plurality of discrete points of theshape character of the target object image and the coordinates of theplurality of discrete points of the template image; and obtaining, by atleast one computer hardware unit, the similarity between the shapecharacter of the target object image and the shape character of thetemplate image according to the first difference and the seconddifference.